Directory UMM :Data Elmu:jurnal:A:Agricultural Economics:Vol24.Issue2.2001:

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Economics of biological control of cassava mealybug in Africa

J. Zeddies

, R.P. Schaab

, P. Neuenschwander

b,∗

, H.R. Herren

b,1

a_{University of Hohenheim, Institute of Agricultural Economics (410 B), D-70593 Stuttgart, Germany} b_{International Institute of Tropical Agriculture (IITA), 08 B.P. 0932, Cotonou, Benin}

Received 24 December 1997; received in revised form 16 February 2000; accepted 9 March 2000

Abstract

Pest populations of the cassava mealybug Phenacoccus manihoti Mat.-Ferr. (Homoptera: Pseudococcidae) were reduced successfully by the biological control agent Apoanagyrus (Epidinocarsis) lopezi De Santis (Hymenoptera: Encyrtidae) throughout most of sub-Saharan Africa. The economics of the project were evaluated based on data from field trials, socio-economic surveys, published results, and financial information provided by the International Institute of Tropical Agriculture (IITA) and the national programmes. Costs and benefits for the biological control of P. manihoti were calculated over 40 years (1974–2013) for 27 African countries, for four different scenarios, taking into account that impact by A. lopezi and speed of the impact differ between ecological zones. A reasonable calculation considering compounded interest resulted in a benefit cost ratio of about 200 when cassava was costed at world market prices, and of about 370–740 when inter-African prices were considered. © 2001 Elsevier Science B.V. All rights reserved.

Keywords: Economics of biological control; Phenacoccus manihoti; Apoanagyrus lopezi; Cassava; Africa

1. Introduction

The cassava mealybug Phenacoccus manihoti Mat.-Ferr. (Homoptera: Pseudococcidae) was first observed in Zaire and Congo in the early 1970s and quickly became the most important pest on cassava. First efforts at biological control against this pest started in 1977. Over the years, P. manihoti spread throughout the entire cassava belt of Africa, with the major exception of Madagascar. The exotic para-sitoid Apoanagyrus (Epidinocarsis) lopezi De Santis (Hymenoptera: Encyrtidae) was released from 1981 onward (Herren et al., 1987). By the end of the

∗_{Corresponding author. Tel.:}₊_{229-35-01-88;} fax:+229-35-05-56.

E-mail address: p.neuenschwander@cgiar.org

(P. Neuenschwander).

1_{Present address: International Centre of Insect Physiology and}

Ecology (ICIPE), Box 30772, Nairobi, Kenya

decade, the biological control agent had spread to all the major mealybug infestations and had brought the pest under control in 95% of all the fields (Herren and Neuenschwander, 1991).

This vast biological control project involving for-eign exploration, quarantine, rearing, release, field and laboratory studies, monitoring, coordination, training, awareness building and impact studies was carried out by the International Institute of Tropi-cal Agriculture (IITA) in collaboration with many other institutions during the last 15 years and counts among the best researched biological control projects (Neuenschwander, 1994, 1996). An early economic analysis of the impact arrived at a rather high return (Norgaard, 1988). It was based on rough estimations and the extrapolation from a few West African data, which demonstrated successful biological control, to the whole continent, where A. lopezi had not yet been established or not yet exerted control.

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The present analysis includes data on damage by P. manihoti also from East Africa. It incorporates the documented impact by A. lopezi and its speed, both of which differ between ecological zones, on the basis of data from many countries, published in the 10 years since Norgaard’s analysis. Monetary benefits accru-ing from increased cassava yields are calculated un-der different assumptions, but environmental and other social benefits are not taken into account. For several realistic scenarios, benefits are compared with costs, which had been mostly covered by donor agencies. Both benefits and costs are discounted over time and the ratio gives an idea of the returns on the investment into this biological control programme.

2. Materials and methods

2.1. Sources for the data base

For the economic analysis, cassava production fig-ures for each country were obtained from Production Yearbooks of the Food and Agricultural Organisation of the United Nations (FAO, 1975–1995). An aver-age yield of 8 tonnes/ha was used as a basis (years 1982–1991, from CIAT, 1993). According to vari-ous country maps and the atlas of cassava for Africa (Carter et al., 1992), a percentage attribution of this production to three zones, namely savanna, rainforest and highlands, was made. Economic data were avail-able from official sources by African governments, as provided to the Food and Agriculture Organisation of the United Nations (FAO, 1975–1995), from IITA’s Collaborative Study on Cassava in Africa (COSCA) (Nweke et al., 1989), and other publications (Lynam, 1987; Dorosh, 1988; Carter et al., 1992; CIAT, 1993). In addition, reports by the German Ministry of Agri-culture and Toepfer International, Hamburg; the Cen-tral Bureau of Statistics, Ministry of Planning and Development in Nairobi, Kenya; Barcley’s Business Guide to Kenya; the Statistics Department, Ministry of Planning and Economic Development and the Con-sumer Price Index, Entebbe, Uganda; as well as others were used (for details see Schaab, 1997).

When P. manihoti invaded Africa it reached ex-tremely high population levels wherever it appeared and became the most important pest insect on cassava within short time. The data concerning crop loss by

the cassava mealybug and crop loss reduction through biological control were obtained by IITA and its col-laborating partners. They have to be seen against the background data on the dynamics of the pest and bene-ficiaries through the years, in the various countries and agroecological zones.

From 1981 onward, A. lopezi was released in about 150 sites in 20 countries. Repeated surveys gave quan-titative data about establishment and spread in the following countries, some of which received A. lopezi by natural dispersal from neighbouring countries without release (north west to south Africa): Senegal, Gambia, Guinea-Bissau, Sierra Leone, Côte d’Ivoire, Ghana, Togo, Benin, Nigeria, Niger, Cameroon, Cen-tral African Republic, Gabon, Congo, Zaire, Rwanda, Burundi, Uganda, Kenya, Tanzania, Malawi, Mozam-bique and Zambia. Spot observations were made in other countries of the cassava belt (reviews in Neuen-schwander, 1994, 1996).

Impact of A. lopezi on the cassava mealybug was rather slow. Often lower mealybug population equi-libria were reached only after several years. Impact was quantified in Ghana, Benin, Nigeria, Gabon, Congo, Zaire, Tanzania, Malawi, and Zambia. Impact of biological control on tuber yield came from station experiments in Nigeria (Schulthess et al., 1991), exper-iments in farmers’ fields in Kenya (Schaab, 1997), and from a survey in Ghana (Neuenschwander et al., 1989). Details on expenses at IITA were obtained from the institute’s financial office. Where necessary, costs of buildings and salaries were proportioned to the space and labour devoted to biological control of cas-sava mealybug (Neuenschwander and Haug, 1992). Costs accruing to countries, whose collaboration was highly subsidized by the project, were estimated from the corresponding donor contracts (for Kenya see Kariuki, 1992).

Farmers had no additional cost related to the bio-logical control project, except for the fact that an in-crease in the quantity harvested caused slightly higher harvest costs.

2.2. Establishing the matrix

The benefits of the biological control project were calculated for each country separately and for dif-ferent scenarios. The following columns were estab-lished in a matrix with lines for each year: Estimated

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number of hectares of cassava harvested per year, production per year, percentage distribution of cas-sava production by ecological zone, spread of P. manihoti through the years within the different eco-logical zones expressed in percentage of the total area under cassava in the country, spread of A. lopezi within the different ecological zones expressed as proportion, damage coefficients from the pest alone (before release of A. lopezi) and when pest and its exotic parasitoid occurred together.

Both damage by P. manihotiand impact by A. lopezi differed from one ecological zone to the other and between years following establishment. Wherever P. manihoti established itself, damage was very high al-ready the same year and losses of 80% (Nwanze, 1982) were computed. Within 5 years, this value was re-duced linearly to 40% in the highlands and savanna and to 20% in the rain-forest. This drop occurred because farmers adapted to the new challenge and planted more tolerant varieties. In addition, indigenous predators, particularly coccinellids, adapted to the new food source and reduced the pest population (Neuen-schwander et al., 1987).

The impact by A. lopezi on P. manihoti was rela-tively slow and stable biological control was achieved only after several years. According to the cited impact studies, the reduction of yield loss due to A. lopezi was computed for each ecological zone in each country as follows (100%=yield, unaffected by P. manihoti): For the savanna, first year 0%, second year 25%, third and subsequent years 37% (out of an average yield loss of 40%); for the forest zone, the corresponding reductions were 0, 10 and 15% (out of an yield loss of 20%). This left a residual damage concentrated in foci of infestation on sandy soils as described from several surveys (Neuenschwander et al., 1990, 1991). For the highlands, reductions of 0, 10, 20, 30, and 35% were computed for the first to the fourth (and subsequent) years (Schaab, 1997). Once a new stable equilibrium was achieved under biological control conditions, the value of crop loss reduction was kept constant for the rest of the 40-year-period considered in this evaluation (1974–2013).

For each zone, these figures were entered for the year the cassava mealybug and A. lopezi had been reported for the first time, respectively. Before this time, losses due to mealybug were computed as being zero.

2.3. Economic analysis

The economic evaluation aimed at determining a benefit cost ratio. Because of uncertainties in some biological and economic parameters, a range of pos-sible results was computed and subjected to a sensi-tivity analysis. The economics of the following four scenarios were investigated.

2.3.1. Additional cassava production

It is assumed that A. lopezi’s action resulted in a country-specific, additional quantity of cassava that could be harvested, as compared to the situation where P. manihoti had caused uncontrolled damage. To es-timate the benefit, crop loss reduction in tonnes was multiplied with a ‘world market price’. Because of the high grain prices in the European Union (Lynam, 1987) and its grain subsidies (Schumacher, 1990) the world market price for cassava is distorted and per-tains to animal feed only. As a rough approximation, it was estimated at US$ 90 per tonne dry weight, from 1995 to 2013.

The US$ 90 per tonne price is, however, too low for cassava traded within Africa. Average price per tonne fresh weight varies a lot from one year to the next and among countries and ranges from about US$ 50 to 100). This translates into US$ 167 to US$ 333 per tonne dry weight (conversion factor fresh to dry weight=0.3), and sometimes even higher. The African prices are used to calculate alternatives to Scenario 1. 2.3.2. Additional cassava under import conditions

In the second scenario, the amount of cassava lost to unchecked damage by P. manihoti was to be imported. Biological control would then reduce this importation. Costs for transport to the interior of the country were added to the farm-gate price (world market level) of the first scenario. The costs for transportation were estimated at about US$ 140 per tonne per 1000 km (US$ 5000 for a 36 tonne trailer for 1000 km). For the calculation, the distance from the nearest harbour to the middle of each country was chosen. Costs were assumed to be constant through the years.

2.3.3. Additional production of an alternative crop, i.e. maize

In this scenario, it was assumed that losses due to P. manihoti were compensated for by locally grown

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maize. A. lopezi’s action reduced this need or allowed the additional maize to be sold. This additional maize production was valued under the assumption of an average yield at the world market price for (yellow) maize (FAO, 1975–1995) adding a quality bonus of 20% for white maize.

2.3.4. Additional maize under import conditions In this scenario, the loss in cassava due to the rav-ages of the mealybug was to be compensated for by importing maize, e.g. as food aid. The reduction of this loss due to biological control was computed using the price of maize under the previous scenario and by adding costs for transport to the interior of the country as in Scenario 2.

All calculations were compounded or discounted by 6% p.a., the investment base being 1994. Costs and benefits are presented in nominal terms as well as at present value (end of 1994). Most funds for this project stem from industrialized countries, where the interest rates rarely exceeded this value during the last two decades. A period of 40 years, i.e. 1974–2013, was chosen as an adequate duration for calculating the economic impact of biological control across the continent. This is the duration usually applied for long lasting projects (including buildings).

For evaluating the total benefit of A. lopezi, the following formula was applied to the data of the spreadsheets:

Benefit= 27

z=1 40

i=1





j=1

PziEzijGij



0.3YziDi

with z the 27 African cassava countries (1=Angola, 2=Benin,. . .,27 =Zambia); I the specific year (1=1974,. . .,40=2013); j the ecological zones (1=savanna, 2=rain forest, 3=highland); Pzi the to-tal cassava production in fresh weight for country z and year i; Ezij the share of cassava production with influence of A. lopezi in country z in year i and zone j; Gij the relative gain (=saved loss) factor in zone j and year i; 0.3 the constant conversion factor from fresh to dry weight; Yzithe price of cassava (or maize substitutes according to scenario) in US dollars per tonne of dried cassava in country z in year i and Di is the discounting/compounding factor for year i.

The total costs for controlling P. manihoti were di-vided into four parts, namely (i) the costs to IITA,

(ii) overhead costs to the donor countries (O) (15% in addition to the IITA expenditures for administration, planning, evaluation, etc.), (iii) costs to African gov-ernments (G) and (iv) costs to African farmers (F), and added up as follows (in US$):

Costs= 40

i=1

(IITAi+Oi +Gi+Fi)Di.

3. Results

3.1. Losses and savings

The economic analyses of biological control of P. manihoti included 27 countries in Africa (Table 1), which together produced about 94% of the total African cassava output in 1995. By this year, all cas-sava growing areas in each country (with the exception of Uganda) had been infected with P. manihoti.

The value of cassava was estimated from the about 9 million ha of cassava harvested in Africa, with an average yield of 8 tonnes/ha. This amounts to 72 mil-lion tonnes of fresh cassava annually. Multiplied with the conversion factor for dried material, i.e. 0.3, gave the 21.5 million tonnes marketable cassava (Table 1). The total benefit of A. lopezi was directly related to the total cassava production per country. The biggest benefits were attributed to Zaire, Nigeria, Ghana, Tanzania, Mozambique and Uganda, which together produced more than 78% of all the cassava involved in the analysis.

The benefits of all 27 countries, accumulated over the 40 years of analysis (Table 2), amounted to US$ 9.4 billion in Scenario 1 (under the assumption of a world market price of US$ 90 per tonne), with an yearly gain of US$ 235 million For the cassava area of about 9 million ha, the reduced loss thus became US$ 26 per ha and year.

Instead of calculating the losses and gains under the assumption of replacement of cassava at world mar-ket price (Scenario 1), three other scenarios were cal-culated. Each assumes a different reaction to the loss caused by the cassava mealybug. While losses var-ied between the scenarios, the impact of A. lopezi re-mained an unchanged percentage. All scenarios are realistic for specific conditions in a particular country, though none would have applied over the entire

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con-Table 1

Basic data from 27 African countries for the economic analysis of the impact of the biological control programme against cassava mealybug Country Percentage of cassava in Cassava production in 1991 First record of

Savanna Rainforest Highlands In 1000 tonne In % P. manihoti A. lopezi

Angola 18 2 80 1850 2.83 1975 1983

Benin 95 5 0 889 1.36 1979 1983

Burundi 0 0 100 580 0.89 1987 1988

Cameroon 29 40 31 1378 2.11 1985 1985

Central African Rep. 75 25 0 520 0.80 1984 1988

Congo 60 40 0 780 1.19 1973 1982

Côte d’Ivoire 40 60 0 1250 1.91 1985 1986

Equatorial Guinea 0 100 0 55 0.08 1989 1989

Gabon 20 80 0 250 0.01 1976 1984

Ghana 67 33 0 3040 4.65 1982 1984

Guinea Bissau 100 0 0 6 0.01 1982 1984

Guinea Conakry 90 0 10 450 0.69 1986 1989

Kenya 29 1 70 650 0.99 1990 1990

Liberia 10 90 0 300 0.46 1990 1990

Malawi 89 1 10 168 0.26 1985 1985

Mozambique 95 >0.5 5 3690 5.65 1986 1988

Niger 100 0 0 216 0.33 1986 1986

Nigeria 15 85 >0.5 20000 30.6 1979 1981

Rwanda 0 0 100 560 0.86 1984 1985

Senegal 100 0 0 14 0.02 1976 1984

Sierra Leone 60 40 0 90 0.14 1982 1985

Tanzania 40 10 50 6266 9.59 1987 1988

Togo 95 5 0 500 0.77 1980 1984

Uganda 5 0 95 3350 5.13 1992 1992

Zaire 45 35 20 18227 27.9 1972 1982

Zambia 5 0 95 270 0.41 1984 1984

All 27 countries 37 42 21 65355 100

tinent. These scenarios can therefore be seen as the vertices of a four-dimensional decision space.

Under import conditions of Scenario 2, the transport of food to the interior of the countries would strongly increase the costs of substitution. Including transport to the interior of all the 27 African countries, the ben-efit by saved investment would increase to approxi-mately US$ 20.2 billion.

If lost cassava would have been replaced by maize, as assumed in Scenario 3, the benefit would have been US$ 8 billion, and with maize food aid in Scenario 4, US$ 14 billion.

3.2. Costs and benefits

The nominal costs of all biological control activi-ties from 1979 (start of the programme) to 2013 were estimated at US$ 34.2 million (Table 2). Compounded /discounted at a rate of 6% relative to the base year

of 1994, they accumulated to a total of US$ 46.9 mil-lion, which was derived as follows: the total costs for IITA related to cassava mealybug biological control amounted to US$ 37.7 million (nominal US$ 27.4 mil-lion). To this, 15% donor agencies’ contributions were added, i.e. US$ 5.7 million compounded (US$ 4.1 mil-lion nominal). Finally, costs of African governments covering expenses for personnel, buildings, electricity, communication, water, and experimental plots were roughly estimated at US$ 100 000 per country over the whole period of the analysis. This hypothetical average for each of the 27 countries amounted to US$ 3.6 mil-lion compounded (US$ 2.7 milmil-lion nominal) in addi-tion to the support received from donors through IITA. Since the donor agencies and the African govern-ments financed the local and the overall campaigns, the African farmers had no expenses for the biological control of P. manihoti.

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Table 2

Costs and benefits (in US$ million) of the biological control project against the cassava mealybug in Africa, with a discount factor of 6% (base year=1994)a

Year Compounding/ discounting factor

Costs Benefits in scenario

To IITA To donors (overheads)

To African governments

Total

Ab Bb A B A B A B 1 2 3 4

1974 3.21 0 0 0 0 0 0 0 0 0 0 0 0

1975 3.03 0. 0 0 0 0 0 0 0 0 0 0 0

1976 2.85 0 0 0 0 0 0 0 0 0 0 0 0

1977 2.69 0 0 0 0 0 0 0 0 0 0 0 0

1978 2.54 0 0 0 0 0 0 0 0 0 0 0 0

1979 2.40 0.30 0.72 0.05 0.12 0 0 0.35 0.84 0 0 0 0

1980 2.26 0.70 1.58 0.11 0.25 0 0 0.81 1.83 0 0 0 0

1981 2.13 1.00 2.13 0.15 0.32 0 0 1.15 2.45 0 0 0 0

1982 2.01 1.00 2.01 0.15 0.30 0 0 1.15 2.31 21 35 16 24

1983 1.90 1.80 3.42 0.27 0.51 0 0 2.07 3.93 98 164 80 117

1984 1.79 2.00 3.58 0.30 0.54 0.20 0.36 2.50 4.48 114 241 135 205

1985 1.69 2.50 4.23 0.38 0.64 0.20 0.34 3.08 5.21 135 314 151 250

1986 1.59 2.00 3.18 0.30 0.48 0.20 0.32 2.50 3.98 329 624 184 343

1987 1.50 1.50 2.25 0.23 0.35 0.20 0.30 1.93 2.90 400 789 196 405

1988 1.42 1.50 2.13 0.23 0.33 0.30 0.43 2.03 2.89 442 890 306 546

1989 1.34 1.32 1.77 0.20 0.27 0.30 0.40 1.82 2.44 408 886 342 600

1990 1.26 1.27 1.60 0.19 0.24 0.30 0.38 1.76 2.22 542 1063 383 666

1991 1.19 1.22 1.45 0.18 0.22 0.30 0.36 1.70 2.02 612 1144 407 699

1992 1.12 1.17 1.31 0.18 0.20 0.20 0.22 1.55 1.73 506 1038 406 699

1993 1.06 1.10 1.17 0.17 0.18 0.20 0.21 1.47 1.56 457 982 396 685

1994 1.00 1.10 0.10 0.16 0.16 0.20 0.20 1.46 1.46 452 968 383 666

1995 0.94 0.80 0.75 0.12 0.11 0.10 0.09 1.02 0.95 395 897 371 648

1996 0.89 0.70 0.62 0.11 0.10 0 0 0.81 0.72 376 854 354 617

1997 0.84 0.60 0.50 0.09 0.08 0 0 0.69 0.58 360 818 339 593

1998 0.79 0.50 0.40 0.08 0.06 0 0 0.58 0.46 343 782 324 568

1999 0.75 0.40 0.30 0.06 0.05 0 0 0.46 0.35 325 742 307 539

2000 0.70 0.30 0.21 0.05 0.04 0 0 0.35 0.25 308 703 291 512

2001 0.67 0.20 0.13 0.03 0.02 0 0 0.23 0.15 292 667 276 486

2002 0.63 0.20 0.13 0.03 0.02 0 0 0.23 0.15 276 633 261 461

2003 0.59 0.20 0.12 0.03 0.02 0 0 0.23 0.14 261 597 247 435

2004 0.56 0.20 0.11 0.03 0.02 0 0 0.23 0.13 246 563 233 411

2005 0.53 0.20 0.11 0.03 0.02 0 0 0.23 0.13 232 531 220 388

2006 0.50 0.20 0.10 0.03 0.02 0 0 0.23 0.12 219 501 207 366

2007 0.47 0.20 0.09 0.03 0.01 0 0 0.23 0.10 207 473 195 345

2008 0.44 0.20 0.09 0.03 0.01 0 0 0.23 0.10 195 446 184 325

2009 0.42 0.20 0.08 0.03 0.01 0 0 0.23 0.09 184 421 174 307

2010 0.39 0.20 0.08 0.03 0.01 0 0 0.23 0.09 173 397 164 290

2011 0.37 0.20 0.07 0.03 0.01 0 0 0.23 0.08 164 375 155 273

2012 0.35 0.20 0.07 0.03 0.01 0 0 0.23 0.08 154 354 146 258

2013 0.33 0.20 0.07 0.03 0.01 0 0 0.23 0.08 146 334 138 243

Total 27.38 37.66 4.15 5.73 2.70 3.61 34.23 47.00 9372 20226 7971 13970

a_{Benefits for four scenarios: 1 — cassava price at farm gate, 2 — cassava price plus transport, 3 — price of local maize as substitute,}

4 — maize price plus transport.

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Table 3

Sensitivity analysis of different scenarios for calculating the losses and benefits of the cassava mealybug biological control project, varying the total duration of the coverage of the calculations (100 years versus 27 years), the discounting/compounding factor (12% p.a. versus 0% p.a.), and the efficiency of Apoanagyrus lopezi (impact half of the one documented)

Variant Standard assumptions (US$ per tonne)a

Duration (years) Discounting/compounding factor

Yield loss reduction by A. lopezi

90 167 333 100 27 12% 0% 50%

Costs in million US$ 47.0 47.0 47.0 48.19 45.56 70.61 33.72 47.00 Benefits in million US$

Scenario 1 9372 17432 34676 11729 6623 8981 11873 6596

Scenario 2 20226 37620 74836 25622 13934 18959 26223 14568

Scenario 3 7971 14826 29493 10201 5371 7335 10539 5855

Scenario 4 13970 25984 51689 17905 9382 12823 18513 10285

Benefit cost ratio

Scenario 1 199 371 738 243 145 127 352 111

Scenario 2 430 800 1592 532 306 268 778 239

Scenario 3 170 315 628 212 118 104 313 94

Scenario 4 297 553 1100 372 206 182 549 165

a_{Duration 40 years, discounting/compounding factor 6%, yield loss reduction depending on ecological zone (about 90%), for three}

different price levels.

The total annual costs for the biological control of P. manihoti peaked in 1985 at US$ 5.2 million, com-pounded to the base 1994 at 6% p.a. From 1985 on-ward, there was a continuous decline in the annual budgets.

Benefit cost ratios for the biological control of P. manihoti varied depending on the different assump-tions. In Scenario 1, costs of US$ 47 million brought returns of US$ 9.4 billion (Table 2), i.e. a benefit cost ratio of 199 through 40 years of analysis. With the same expenses, benefit cost ratios were 430 in Scenario 2, 170 in Scenario 3 and 297 in Scenario 4 (Table 3).

3.3. Sensitivity analyses

Since many data were based on uncertain assump-tions, sensitivity analyses were carried out by varying some of those parameters (Table 3).

The most important factor affecting the cost-benefit analysis is the initial assumption about the cost of the commodity. In the preceding analysis, all benefits were based on a world market price of cassava. Within Africa, where most of the cassava from this continent is traded, prices of cassava are, however, much higher. Thus, with a conservative price of US$ 167 per tonne dry weight, the benefit in Scenario 1 amounted to US$

17.4 billion and the benefit cost ratio became 371. With the higher commodity price of US$ 333 per tonne the benefits became US$ 34.7 billion and the benefit cost ratio 738.

For various discounting rates, the total benefit changed from US$ 9.0 billion with 12% p.a. to US$ 11.9 billion with close to 0% p.a., and the benefit cost ratio from 127 to about 352.

If A. lopezi was assumed to cut yield loss (of 40%) in half, instead of the reduction by about 9/10th used in the base line analysis, the benefit cost ratio would still be 111.

By contrast, if the analysis was extended for 100 years, the benefit cost ratio would only rise to 243 as compared to a benefit cost ratio of 145 under a 27-year-duration (up to the end of the year 2000).

The results for other scenarios are given in Table 3. They indicate that even with pessimistic as-sumptions this biological control project would still remain highly profitable.

4. Discussion

Complete economic analyses of biological control projects are rare. One famous example, the complete control of the rhodesgrass mealybug by the encyrtid Neodusmetia sangwani (Rao), featured an evaluation

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of the loss in turfgrass, partially compensated for by insecticide treatments and the reduction in head of cat-tle feeding on susceptible grasses (Dean et al., 1979). Analysis was done on an yearly basis and not com-pounded. Returns within 1 year were far greater than the outlays of the research station or the costs of con-trol with insecticides. This evaluation was done in an economic environment where all costs and benefits could be labelled quite clearly. The calculations did not, however, reflect the costs accruing because insect populations treated with insecticides are likely to de-velop pesticide resistance (Gutierrez et al., 1979).

While impact of biological control could also be assessed in terms of reduced use of insecticides in Asian rice systems (Fox, 1991; Kenmore, 1991), this is not possible for African small holder farms, where no insecticides are used on cassava.

In the present study, all statistical figures reported and cited are less clear than in the above examples. National cassava production and average prices are extremely difficult to estimate, because of a high regional diversity of cropping patterns and fluctua-tions in yield (Nweke, 1996a). Yield measurements on farmers’ fields are uncommon and the price of cassava, which is traded freely, is rather volatile. Eval-uating the economic benefits of a biological control project across all of Africa is therefore difficult and can yield broad estimates only.

In all scenarios, changes in prices caused by the re-duction in supply and, after biological control had been established, the increase in supply were not considered directly and prices were kept constant. All the other factors affecting cassava prices, like quality, process-ing technology, market access, etc. (Nweke, 1996a, b; Prudencio et al., 1992) were not included either. The influence of these fluctuations was, however, gauged by comparing scenarios with different prices. Thus, in scenario 1, the benefit cost ratio is about 200 with the low world market price and about 370 and 740 with low and high price levels, respectively, as they are paid at different times in the inter-African market.

Cassava has become a prime cash crop across much of Africa (Nweke, 1996a) and better processing tech-nologies allow further expansion of cassava (Nweke, 1996b). Scenario 1 of the present study, with local in-crease in cassava production attributed to A. lopezi, seems therefore the most realistic one for areas where land reserves are still available. Scenario 2 would

ap-ply to those countries or regions, which responded to the mealybug disaster by importing cassava from neighboring countries. Scenario 3 applies to the re-gions, where a collapse of cassava production neces-sitated a change to increased maize production. This scenario turns out to be cheaper than Scenario 1. On paper this may be so, but since dietary habits of peo-ples are a powerful force, it does not follow from this price differential that maize would be preferred. Also, it assumes that indeed more maize could have been produced, which depends on soil and climate. Cassava being the far hardier plant than maize, this scenario becomes unrealistic under harsh conditions. Its main attraction is the fact that maize has a well established world market price. Scenario 4, with import of maize as food aid, applied to the early years of mealybug infestation and only to a few countries (Pelletier and Msukwa, 1990). It must, therefore, be stressed that none of the scenarios is equally likely for all countries and all years, and the evaluation for some of the coun-tries might be better served by still other scenarios. Thus, alternatives for cassava would be rice for Côte d’Ivoire, yams for Nigeria, banana for Uganda, and so on, while no alternative might exist for the Democratic Republic of Congo.

The economic impact of biological control of the cassava mealybug had been analyzed before (Nor-gaard, 1988), based on rough estimations, as the author conceded himself, and the extrapolation of a few West African data over the whole continent. The Norgaard study did not have access to the detailed country in-formation and ecological background, which became available for the present analysis. The present study goes further by investigating how the loss in cassava production could be valued under different circum-stances. Different scenarios are presented and sensi-tivity analyses made. Another difference concerns the longer time frame (40 as compared to 25 years) and a lower interest rate (6% with 1994 as base year versus 10%, with 1982 as base year) of the present study. We contend that the present choices of time frame and in-terest rates are based on common practice and justified by the long-term nature of the project. Thus, for in-stance for 1991, the average interest rate on new com-mitments for the private sector was 7.6% according to the World Bank Annual Report 1992, and agroforestry projects have commonly been evaluated within a 50 year time frame (Engelhardt, 1989). Both parameters,

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duration and interest rates, were then tested in the sen-sitivity analysis. With longer time frames it was shown that any additional revenue accruing beyond 40 years was negligible. Even with the unrealistically high in-terest rate of 12%, the project would still have given a high benefit cost ratio.

In the present study, Norgaard’s assumption that the impact of biological control tapers off was rejected as a general concept. Adaptation of indigenous coccinel-lids to the new food source, P. manihoti, which led to an early reduction of pest damage, was, however, in-cluded in the calculations. While this initial reduction of P. manihoti by indigenous coccinellids was con-siderable, the impact of local predators proved to be much smaller once biological control by A. lopezi was established and mean population levels of P. manihoti were low (Gutierrez et al., 1988). We know of no case where this biological equilibrium between P. manihoti and its natural enemies, including A. lopezi, was aban-doned and where long-term biological control would have failed. We, therefore, see no reason to have the effect of A. lopezi taper off after this initial adjustment. Norgaard’s costs of US$ 14.8 million were much smaller than assumed here, because no donor over-heads had been included, and cost attribution within IITA had been done differently and more favourably for a good return. His benefits were US$ 2.2 billion and the benefit cost ratio was 149. This result is lower than the present sensitivity analysis and comes quite close to the benefit cost ratio of 145 calculated for the pessimistically short period 1974–2000.

In conclusion, the present analysis was based on much more reliable data than those available to Nor-gaard, (1988), who visited IITA as a consultant at the beginning of the biological control programme. After correcting some wrong assumptions, improving the time frame of impact of A. lopezi, putting costs on a broader and more realistic basis, developing some realistic scenarios of reaction by the farmers and gov-ernments to the mealybug disaster, the actual benefit cost ratios of the present study are higher, but not vastly different from Norgaard’s. In both the studies, the internal rates of return (261, 328, 245, 289%, re-spectively, for the four scenarios of the present study) are extremely high.

The present evaluation must be judged as conserva-tive. P. manihoti does more damage under conditions prevailing in the savanna zones than in the forest.

Biological control by A. lopezi brings correspondingly higher return in the savanna (Neuenschwander et al., 1989). Proportioning cassava to the different ecologi-cal zones was based on maps. This underestimates the contribution to crop loss reduction from the savanna zones on two counts. First, cassava production is mov-ing into ever drier areas (Nweke et al., 1994); second, areas with true rainforest conditions are shrinking at a fast pace, so that the area covered by rainforest is now much reduced by comparison to the maps used (Sayer et al., 1992). Finally, the chosen parameters did not take into account the observed intensifica-tion of cassava culture (Nweke and Spencer, 1995), which would mean higher yields, but also higher po-tential losses, and higher savings due to biological control.

The present evaluation was made at a time when an area producing about 3 million tonnes of cassava per year, including Madagascar, had not yet been infested by P. manihoti. Further spread to India and the rest of Asia remains a threat, which has to be delayed as long as possible by careful quarantine inspection. For these areas, biological control by A. lopezi constitutes a technology on the shelf, which can be called for at little cost.

In the present project analysis, secondary costs, like basic research, training, collaboration with national programmes, etc., were incorporated. The total costs divided through the 9 million ha of cassava in Africa would result in a single treatment of US$ 5.2 per ha. Divided over the 40 years of the analysis, yearly costs amount to 13 US-cents per ha.

Beside the benefit of higher yield of cassava tubers, as evaluated in the present study, there is also a higher leaf yield for African families eating cassava leaves as vegetable. Another positive side effect of the biologi-cal control, not incorporated into the evaluation, was the higher yield of stems, which are sometimes also used for fuel (Schaab, 1997).

Finally, the benefit of maintaining a healthy en-vironment through the use of biological control can only be stated in words, but should be recognized as a benefit. Current efforts at expressing benefits to the environment in monetary terms have generally been confronted with difficulties and the need for a more strategic approach to ecological impact assessment has been identified (Treweek, 1996). While some projects might have to balance short-term monetary

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gains against long-term benefits of sustainability, this antagonism did not materialize in the present bio-logical control project. There simply was no short-term answer since all potential alternatives were generally not effective and not feasible under the given conditions. The project has thus become a good example in the use of technologies that substitute for external inputs, thereby guaranteeing sustainability (Meerman et al., 1996).

Acknowledgements

We are grateful for the financial support from the Gesellschaft für technische Zusammenarbeit (GTZ) to Ralf Schaab and the aid agencies of Austria, Denmark, Germany, Italy, Norway, Switzerland, The Nether-lands, USA, the African Development Bank and the International Fund for Agricultural Development for their generous support to IITA. We would like to thank our colleagues at IITA, Benin and Ibadan, H. de Groote and F. Nweke, and the Institute of Farm Management, University of Hohenheim, particularly F. Litzka, for their cooperation and comments.

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